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<p>Recommender systems are central to modern digital platforms, yet interaction-only approaches often struggle with sparsity, cold-start users/items, and limited interpretability in domains where rich textual and semantic signals are available. We present a hybrid framework that uses large language models (LLMs) to construct and enrich a domain-specific knowledge graph (KG) from item-side text, and then injects the resulting semantic structure into downstream recommendation models. The proposed pipeline follows a two-phase design. In Phase&#xa0;1, we induce a movie-domain KG from MovieLens metadata and textual signals, align it to external resources, and evaluate KG quality through complementary intrinsic strategies covering exact matching, structural diagnostics, and embedding-based semantic similarity. In Phase&#xa0;2, we learn KG-derived embeddings (including translational and bilinear variants) and integrate them into a broad set of state-of-the-art recommenders spanning collaborative filtering, feature-interaction models, and graph-based models. Across models and metrics, KG-augmented representations yield consistent improvements in top-<i>K</i> ranking quality (e.g., Recall@<i>K</i>, NDCG@<i>K</i>, Precision@<i>K</i>). Beyond end-to-end accuracy, we analyze how intrinsic KG quality signals relate to downstream recommendation gains, and we distill practical guidance on when LLM-generated KGs are most beneficial. Overall, this provides a reusable pipeline and empirical evidence that coupling LLM-based knowledge induction with structured KG reasoning can improve both recommendation effectiveness and interpretability.</p>

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Large language model enhanced embeddings for knowledge aware recommender systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Het Darshan Mehta,
  • Marco Polignano,
  • Giovanni Semeraro,
  • Ernesto William De Luca

摘要

Recommender systems are central to modern digital platforms, yet interaction-only approaches often struggle with sparsity, cold-start users/items, and limited interpretability in domains where rich textual and semantic signals are available. We present a hybrid framework that uses large language models (LLMs) to construct and enrich a domain-specific knowledge graph (KG) from item-side text, and then injects the resulting semantic structure into downstream recommendation models. The proposed pipeline follows a two-phase design. In Phase 1, we induce a movie-domain KG from MovieLens metadata and textual signals, align it to external resources, and evaluate KG quality through complementary intrinsic strategies covering exact matching, structural diagnostics, and embedding-based semantic similarity. In Phase 2, we learn KG-derived embeddings (including translational and bilinear variants) and integrate them into a broad set of state-of-the-art recommenders spanning collaborative filtering, feature-interaction models, and graph-based models. Across models and metrics, KG-augmented representations yield consistent improvements in top-K ranking quality (e.g., Recall@K, NDCG@K, Precision@K). Beyond end-to-end accuracy, we analyze how intrinsic KG quality signals relate to downstream recommendation gains, and we distill practical guidance on when LLM-generated KGs are most beneficial. Overall, this provides a reusable pipeline and empirical evidence that coupling LLM-based knowledge induction with structured KG reasoning can improve both recommendation effectiveness and interpretability.